We consider the problem of scheduling a set of preemptible independent periodic implicit-deadline hard real-time tasks on heterogeneous processors. We divide this problem into two sub-problems: (a) assigning portions of each processor (offline) to each task without jeopardizing schedulability; and (b) generating a schedule satisfying the assigned portions using an online semi-partitioned scheduler, called Heterogeneous Quasi-Partitioned Scheduling (hQPS). The scheduler handles task servers at run-time for ensuring that the processor shares assigned to tasks are timely available to them. Assessments indicate that the proposed solution (i) has good scalability (up to 64 tasks, 64 processors), (ii) is effective in generating schedules with few preemptions and few migrations, and (iii) is effective in managing resources; for task sets where an extra processor speed is required, our solution needs at most 10% extra compared to an optimal scheduler.

Quasi-Partition Scheduling (QPS) is a new scheduling approach with low preemption and migration overhead. QPS was originally proposed taking into consideration hard periodic and sporadic tasks. In this paper we discuss a proposal for extending QPS to deal with soft aperiodic tasks.

We describe a new algorithm, called Quasi-Partitioned Scheduling (QPS), capable of scheduling any feasible system composed of independent implicit-deadline sporadic tasks on identical processors. QPS partitions the system tasks into subsets, each of which is either scheduled by EDF on a single processor or by a set of servers on two or more processors. More precisely, QPS uses an efficient scheme to switch between partitioned EDF and global-like scheduling rules in response to system load variation, providing dynamic adaptation in the system. Extensive simulation compares QPS favorably against related work, showing that it has very low preemption and migration overheads.

Optimal multiprocessor real-time schedulers incur significant overhead for preemptions and migrations. We present RUN, an efficient scheduler that reduces the multiprocessor problem to a series of uniprocessor problems. RUN significantly outperforms existing optimal algorithms with an upper bound of O(log m) average preemptions per job on m processors (≤ than 3 per job in all of our simulated task sets) and reduces to Partitioned EDF whenever a proper partitioning is found.

The problem of scheduling a set of tasks on a multiprocessor architecture is addressed. Tasks are assumed to be sporadic with arbitrary deadlines and may migrate between processors. The execution of migrating tasks is controlled by a bandwidth reservation scheme so that schedulability is guaranteed by EDF. Task migration costs are taken into consideration. Results from experiments indicate that the proposed approach performs well in terms of schedulability.